#devtools::install_github(repo="haozhu233/kableExtra", ref="a6af5c0") #you have to install this specific version of kableextra
#This says rvest might have broke it https://github.com/haozhu233/kableExtra/issues/595

library(tidyverse)
## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.1 ──
## ✓ ggplot2 3.3.5     ✓ purrr   0.3.4
## ✓ tibble  3.1.3     ✓ dplyr   1.0.7
## ✓ tidyr   1.1.3     ✓ stringr 1.4.0
## ✓ readr   2.0.0     ✓ forcats 0.5.1
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## x dplyr::filter() masks stats::filter()
## x dplyr::lag()    masks stats::lag()
library(kableExtra)
## 
## Attaching package: 'kableExtra'
## The following object is masked from 'package:dplyr':
## 
##     group_rows
library(tidyverse)

restartspark <- function(){
  #devtools::install_github("rstudio/sparklyr")
  #devtools::install_github("rstudio/sparklyr")
  #install.packages('sparklyr') #rolling back to the stable version
  library(sparklyr)
  #spark_available_versions()
  #spark_installed_versions()
  #spark_uninstall(version="3.0.1", hadoop_version="3.2")
  #spark_uninstall(version="2.4.3", hadoop_version="2.7")
  #oh interesting the default is spark 2.4.3 I wonder why that is
  #Error: Java 11 is only supported for Spark 3.0.0+
  #spark_install("3.0") #3.1.1 is currently the latest stable, but 3.0 is the latest available
  
  #library(geospark)
  #library(arrow)
  mem="160G"
  try({spark_disconnect(sc)})
  conf <- spark_config()
  #conf$`sparklyr.cores.local` <- 128
  #https://datasystemslab.github.io/GeoSpark/api/sql/GeoSparkSQL-Parameter/
  conf$spark.serializer <- "org.apache.spark.serializer.KryoSerializer"
  #conf$spark.kryo.registrator <- "org.datasyslab.geospark.serde.GeoSparkKryoRegistrator"
  #conf$spark.kryoserializer.buffer.max <- "2047MB" #Caused by: java.lang.IllegalArgumentException: spark.kryoserializer.buffer.max must be less than 2048 mb, got: + 10240 mb.
  #https://github.com/DataSystemsLab/GeoSpark/issues/217
  #conf$geospark.global.index <- "true"
  #conf$geospark.global.indextype <- "quadtree"
  #conf$geospark.join.gridtype <- "kdbtree"
  #conf$spark.sql.shuffle.partitions <- 1999 #https://github.com/DataSystemsLab/GeoSpark/issues/361 #setting to just under 2k so compression doesn't kick in, don't need to lower the memory footprint
  conf$spark.driver.maxResultSize <- "100G"
  conf$spark.memory.fraction <- 0.9
  conf$spark.storage.blockManagerSlaveTimeoutMs <-"6000000s" #Failed during initialize_connection: java.lang.IllegalArgumentException: requirement failed: spark.executor.heartbeatInterval should be less than or equal to spark.storage.blockManagerSlaveTimeoutMs
  conf$spark.executor.heartbeatInterval <-"6000000s"# "10000000s"
  conf$spark.network.timeout <- "6000001s"
  conf$spark.local.dir <- "/mnt/8tb_b/spark_temp/"
  conf$spark.worker.cleanup.enabled <- "true"
  conf$"sparklyr.shell.driver-memory"= mem
  conf$'spark.driver.maxResultSize' <- 0 #0 is ulimmited
  
  conf$'spark.sql.legacy.parquet.datetimeRebaseModeInRead' <- 'LEGACY'
  conf$'spark.sql.legacy.parquet.datetimeRebaseModeInWrite' <- 'LEGACY'
  
  conf$'spark.sql.execution.arrow.maxRecordsPerBatch' <- "5000000" #https://github.com/arctern-io/arctern/issues/399
  
  #Error: org.apache.spark.sql.AnalysisException: The pivot column variable_clean has more than 10000 distinct values, this could indicate an error. If this was intended, set spark.sql.pivotMaxValues to at least the number of distinct values of the pivot column.;
  conf$'spark.sql.pivotMaxValues' <- "5000000"
  
  sc <<- spark_connect(master = "local", config = conf#,
                       #version = "2.3.3" #for geospark
  ) 
}

restartspark()
## 
## Attaching package: 'sparklyr'
## The following object is masked from 'package:purrr':
## 
##     invoke
## The following object is masked from 'package:stats':
## 
##     filter
## Error in spark_disconnect(sc) : object 'sc' not found
yid_test <- spark_read_parquet(sc, path="/mnt/8tb_a/rwd_github_private/TrumpSupportVaccinationRates/results/performance/" ,memory=F) 

errors_ablation_fold <- yid_test %>% collect() %>% group_by(ablation, fold) %>% summarise(rmse=Metrics::rmse(y_share18plus,y_hat_test_pruned_optimized), mae=Metrics::mae(y_share18plus,y_hat_test_pruned_optimized) )
## `summarise()` has grouped output by 'ablation'. You can override using the `.groups` argument.
errors_ablation <- yid_test %>% collect() %>% group_by(ablation) %>% summarise(rmse=Metrics::rmse(y_share18plus,y_hat_test_pruned_optimized), mae=Metrics::mae(y_share18plus,y_hat_test_pruned_optimized) )

performance_ablation <- yid_test %>% collect() %>% group_by(ablation) %>% summarize(rmse_y_hat_test=Metrics::rmse(y_hat_test_pruned_optimized, y_share18plus), rae_y_hat_test=Metrics::mae(y_hat_test_pruned_optimized, y_share18plus))  
#performance_ablation %>% View()
performance_ablation %>% ggplot(aes(x=ablation %>% as.factor(), y=rmse_y_hat_test)) + geom_point() + coord_flip()

performance_ablation %>% ggplot(aes(x=ablation %>% as.factor(), y=rae_y_hat_test)) + geom_point() + coord_flip()

residuals <- yid_test %>% dplyr::select(ablation, fips, y_share18plus, y_hat_test_pruned_optimized) %>% collect() %>% mutate(residual=y_hat_test_pruned_optimized-y_share18plus) %>% mutate(fips_state = round(fips/1000 ))

residuals_state <- residuals %>% group_by(ablation, fips_state) %>% summarise(residual=mean(residual))
## `summarise()` has grouped output by 'ablation'. You can override using the `.groups` argument.
residuals_state_diff <- residuals_state %>% 
                         left_join(residuals_state %>% mutate(ablation=ablation-1) %>% rename(residual_withoutinfo=residual) ) %>% 
                         mutate(state_residual_change_from_adding_info= round(abs(residual)-abs(residual_withoutinfo),4) ) #make sure you point this the right direction because error is bad
## Joining, by = c("ablation", "fips_state")
states_sf_tigris_continental <- readRDS( "/mnt/8tb_a/rwd_github_private/TrumpSupportVaccinationRates/data_out/states_sf_tigris_continental.Rds") %>%
  janitor::clean_names() %>% mutate(fips_state=statefp %>% as.numeric() )

for(i in (unique(residuals_state_diff$ablation )-1 )  ){
  p_test_folds <- 
    states_sf_tigris_continental  %>%
    left_join(residuals_state_diff %>% filter(ablation==i)) %>%
    ggplot(aes(fill = state_residual_change_from_adding_info  )) +
    geom_sf() + 
    #ggtitle("1") +
    scale_fill_gradient2("Changes\nMAE",midpoint=0, trans="reverse") + xlab("") + ylab("") +
    theme_bw() + 
    theme(legend.position = c(0.90, 0.25)) +
    theme(axis.title.x=element_blank(),
          axis.text.x=element_blank(),
          axis.ticks.x=element_blank()) +
    theme(axis.title.y=element_blank(),
          axis.text.y=element_blank(),
          axis.ticks.y=element_blank())
  p_test_folds
  
  ggsave(filename=glue::glue("/mnt/8tb_a/rwd_github_private/TrumpSupportVaccinationRates/docs/plots/p_ablation_states_{i}.png"), plot = p_test_folds , height=6, width=12)
  
}
## Joining, by = "fips_state"
## Joining, by = "fips_state"
## Joining, by = "fips_state"
## Joining, by = "fips_state"
## Joining, by = "fips_state"
## Joining, by = "fips_state"
## Joining, by = "fips_state"
## Joining, by = "fips_state"
## Joining, by = "fips_state"
## Joining, by = "fips_state"
## Joining, by = "fips_state"
## Joining, by = "fips_state"
## Joining, by = "fips_state"
## Joining, by = "fips_state"
## Joining, by = "fips_state"
## Joining, by = "fips_state"
## Joining, by = "fips_state"
## Joining, by = "fips_state"
## Joining, by = "fips_state"
## Joining, by = "fips_state"
## Joining, by = "fips_state"
## Joining, by = "fips_state"
## Joining, by = "fips_state"
  i=residuals_state_diff$ablation %>% max()
  p_test_folds <- 
    states_sf_tigris_continental  %>%
    left_join(residuals_state_diff %>% filter(ablation==i)) %>%
    mutate(state_residual_change_from_adding_info=NA)  %>%
    ggplot(aes(fill = state_residual_change_from_adding_info  )) +
    geom_sf() + 
    #ggtitle("1") +
    scale_fill_gradient2("Changes\nMAE",midpoint=0, trans="reverse") + xlab("") + ylab("") +
    theme_bw() + 
    theme(legend.position = c(0.90, 0.25)) +
    theme(axis.title.x=element_blank(),
          axis.text.x=element_blank(),
          axis.ticks.x=element_blank()) +
    theme(axis.title.y=element_blank(),
          axis.text.y=element_blank(),
          axis.ticks.y=element_blank())
## Joining, by = "fips_state"
  p_test_folds

  ggsave(filename=glue::glue("/mnt/8tb_a/rwd_github_private/TrumpSupportVaccinationRates/docs/plots/p_ablation_states_{i}.png"), plot = p_test_folds , height=6, width=12)
restartspark()
treeshap_all <- spark_read_parquet(sc, name="treeshap_all",path="/mnt/8tb_a/rwd_github_private/TrumpSupportVaccinationRates/results/shap/" ,memory=F) 
sdf_nrow(treeshap_all)
## [1] 7644852
treeshap_all %>% head()
## Warning in sdf_collect_static(object, impl, ...): NAs introduced by coercion to
## integer range
#4 causes the first error, 5 too, 6 too

number_features <- treeshap_all %>%
                   dplyr::select(ablation, test_fold,variable) %>%
                   sdf_distinct() %>%
                   count(ablation, test_fold) %>%
                   collect() %>%
                   group_by(ablation) %>%
                   summarise(feature_count_min=min(n), feature_count_mean=mean(n), feature_count_max=max(n))

used_vars_df <- treeshap_all %>% 
  dplyr::group_by(ablation, k_smallest, variable) %>% 
    summarise(shap_variable_total= shap %>% abs() %>% sum() ) %>%
  dplyr::group_by(ablation,  k_smallest) %>% 
    mutate(shap_cluster_total=shap_variable_total %>% abs() %>% sum()) %>%
  ungroup() %>%
  #filter(shap_cluster_total==max(shap_cluster_total)) %>% 
  dplyr::arrange(ablation,shap_cluster_total %>% desc(),shap_variable_total %>% desc() ) %>%
  collect()
## Warning: Missing values are always removed in SQL.
## Use `SUM(x, na.rm = TRUE)` to silence this warning
## This warning is displayed only once per session.
## Warning: Missing values are always removed in SQL.
## Use `SUM(x, na.rm = TRUE)` to silence this warning
## This warning is displayed only once per session.
rhs_codebook_total_clustered <- readRDS("/mnt/8tb_a/rwd_github_private/TrumpSupportVaccinationRates/data_out/rhs_codebook_total_clustered.Rds")


top_5_vars_by_abblation <- used_vars_df %>% group_by(ablation) %>% slice_head(n = 5) %>%
  filter(shap_cluster_total==max(shap_cluster_total)) %>%
  left_join(rhs_codebook_total_clustered %>% dplyr::select(variable=variable, description  )) %>%
  mutate(description_clean = description %>%
           str_replace_all("_",' ') %>% 
           str_replace_all("donaldjtrump 2020",'Trump Vote AAAA') %>%
           str_replace_all("donaldtrump 2016",'Trump Vote BBBB') %>%
           str_replace_all("[0-9]{4}",'') %>% 
           str_replace_all("AAAA",'2020') %>%
           str_replace_all("BBBB",'2016') %>%
           str_replace_all("perc$|perc |percent of |per capita |percent ",'') %>% 
           str_replace_all("percap",'') %>% 
           str_replace_all("change in people ",'Δ ') %>% 
           str_replace_all("\\(.*?\\)",' ') %>% 
           str_replace_all("--total number of adherents",' ') %>% 
           str_replace_all(" years and over",'<') %>% 
           str_replace_all(" under ",'≥') %>% 
           str_replace_all("  ",' ') %>% 
           str_replace_all(" ,",',') %>%
           trimws()
         )
## Joining, by = "variable"
#top_5_vars_by_abblation %>% View()


#install.packages('formattable')
library(formattable)
top_5_vars_by_abblation %>% 
  mutate(shap_variable_total =  color_bar("lightgreen")(shap_variable_total %>% round())  ) %>%
  dplyr::select(ablation,k_smallest,description_clean,shap=shap_variable_total) %>%
  mutate(ablation=ablation %>% as.character()) %>%
  mutate(k_smallest=k_smallest %>% as.character()) %>%
  
  kbl(format='html',booktabs = TRUE, longtable = TRUE, escape = F, align = 'c' ) %>% #
  column_spec(3, width = "10cm" ) %>%
  column_spec(4, width = "1cm" ) %>%
  collapse_rows(columns = 1:2, valign = "top")
ablation k_smallest description_clean shap
1 130 WARNING DV ON RHS people partial covidactnow 18plus 68
WARNING DV ON RHS people partial vaccinetracking 18plus 39
WARNING DV ON RHS people complete usatoday 18plus 4
2 141 who self report somewhat/strongly support providing tax rebates for people who purchase energy-efficient vehicles or solar panels 38
who self report somewhat/strongly support regulating CO2 as a pollutant 1
who self report somewhat/strongly support requiring utilities to produce 20% electricity from renewable sources 1
3 140 politics Trump Vote 2016 30
politics Trump Vote 2020 15
4 121 who self report somewhat/strongly agree that global warming is affecting the weather in the United States 28
who self report somewhat/strongly disagree that global warming is affecting the weather in the United States 26
who self report think their local officials should be doing more/much more to address global warming 10
who self report somewhat/strongly disagree that schools should teach about the causes, consequences, and potential solutions to global warming 6
who self report think Congress should be doing more/much more to address global warming 2
5 132 Total fields of bachelor’s degrees reported, total, science and engineeringsocial sciences 20
Total fields of bachelor’s degrees reported, total, arts, humanities, and otherliterature and languages 6
6 57 Health insurance coverage status by age, total, 19 to 64 years, no health insurance coverage 19
Health insurance coverage status by age, total,≥19 years, no health insurance coverage 4
Health insurance coverage status by age, total, 19 to 64 years, no health insurance coverage 1
7 83 religion evangelical lutheran church in america–total number of congregations 5
religion mainline protestant–total number of congregations 5
religion evangelical protestant 4
religion mainline protestant 4
religion seventh-day adventist church 2
8 20 Place of birth by educational attainment in the united states, total, graduate or professional degree 13
Place of birth by age in the united states, total, native; born outside the united states, 25 to 34 years 2
Place of birth by educational attainment in the united states, total, born in other state in the united states, high school graduate 2
Place of birth by educational attainment in the united states, total, born in state of residence, graduate or professional degree 2
Place of birth by age in the united states, total, born in other state in the united states,≥5 years 1
9 21 Geographical mobility in the past year by educational attainment for current residence in the united states, total, graduate or professional degree 10
Geographical mobility in the past year by educational attainment for residence 1 year ago in the united states, total living in area 1 year ago, same house, graduate or professional degree 4
Geographical mobility in the past year by educational attainment for current residence in the united states, total, moved within same county, bachelor’s degree 2
Geographical mobility in the past year by age for current residence in the united states, total, moved from different state, 1 to 4 years 2
Geographical mobility in the past year by marital status for residence 1 year ago in the united states, total living in area 1 year ago, moved to different county within same state, now married, except separated 2
10 87 Δ employed by Private in NAICS 72 Accommodation and food services 14
Δ employed by Private in NAICS 71 Arts, entertainment, and recreation 2
people employed by Federal Government in NAICS 48-49 Transportation and warehousing 1
Δ employed by Private in NAICS 23 Construction 1
people employed by Private in NAICS 11 Agriculture, forestry, fishing and hunting 0
11 123 people who self report masking always 13
people who self report masking never 4
people who self report masking rarely 2
people who self report masking sometimes 2
12 38 Field of bachelor’s degree for first major for the population 25 11
Sex by age by field of bachelor’s degree for first major for the population 25 3
Sex by age by field of bachelor’s degree for first major for the population 25 2
Sex by age by field of bachelor’s degree for first major for the population 25 2
Sex by age by field of bachelor’s degree for first major for the population 25 1
13 56 Mortgage status, total, housing units with a mortgage, contract to purchase, or similar debt, with either a second mortgage or home equity loan, but not both 6
Mortgage status, total, housing units with a mortgage, contract to purchase, or similar debt, with either a second mortgage or home equity loan, but not both, home equity loan only 5
Mortgage status by real estate taxes paid, total, not mortgaged, no real estate taxes paid 3
Mortgage status by real estate taxes paid, total, not mortgaged, less than $800 3
Mortgage status by real estate taxes paid, total, with a mortgage, no real estate taxes paid 2
14 89 flu vaccinations medicare 8
vaccine supply locations walkin 2
people self report would accept vaccination 1
COVID infections total infected mean 1
COVID infections ratio estimated cases to measured 1
15 60 Health insurance coverage status and type by work experience, total, worked less than full-time, year-round, with health insurance coveragewith medicaid/means-tested public coverage 9
Health insurance coverage status and type by work experience, total, worked full-time, year-round, no health insurance coverage 2
Health insurance coverage status and type by work experience, total, worked full-time, year-round, with health insurance coveragewith medicaid/means-tested public coverage 1
Health insurance coverage status and type by employment status, total, not in labor force, no health insurance coverage 1
Health insurance coverage status and type by work experience, total, worked less than full-time, year-round, no health insurance coverage 1
16 46 Veteran status by educational attainment for the civilian population 25 12
Service-connected disability-rating status and ratings for civilian veterans 18 1
Age by veteran status by employment status for the civilian population 18 to 64 years, total, 35 to 54 years, nonveteran, in labor force 1
17 61 Health insurance coverage status and type by citizenship status, total, native born, no health insurance coverage 14
Health insurance coverage status by living arrangement, total, in family households, in married couple families, no health insurance coverage 1
18 138 who self report somewhat/strongly oppose drilling for oil in the Arctic National Wildlife Refuge 9
who self report somewhat/strongly oppose expanding offshore drilling for oil and natural gas off the U.S. coast 5
who self report somewhat/strongly support drilling for oil in the Arctic National Wildlife Refuge 1
who self report somewhat/strongly support expanding offshore drilling for oil and natural gas off the U.S. coast 1
GDP in mining, quarrying, and oil and gas extraction 0
19 62 Citizen, voting-age population by educational attainment, total, graduate or professional degree 15
Citizen, voting-age population by educational attainment, total, some college, no degree 1
20 109 Educational attainment for the population 25 20
Educational attainment for the population 25 0
Educational attainment for the population 25 0
21 98 Types of health insurance coverage by age, total, 35 to 64 years, with one type of health insurance coverage, with medicaid/means-tested public coverage only 4
Types of health insurance coverage by age, total, 35 to 64 years, no health insurance coverage 3
Types of health insurance coverage by age, total, 19 to 34 years, with one type of health insurance coverage, with medicaid/means-tested public coverage only 2
Types of health insurance coverage by age, total, 35 to 64 years, with one type of health insurance coverage, with va health care only 2
Health insurance coverage status and type by age by educational attainment, total, 26 to 64 years, some college or associate’s degree 2
22 36 Sex by educational attainment for the population 25 6
Sex by age by educational attainment for the population 18 4
Sex by educational attainment for the population 25 3
Sex by educational attainment for the population 25 2
Sex by age by educational attainment for the population 18 1
23 133 Educational attainment by presence of a computer and types of internet subscription in household, total, bachelor’s degree or higher, has a computer, with a broadband internet subscription 18
Educational attainment by presence of a computer and types of internet subscription in household, total, bachelor’s degree or higher 5
Educational attainment by presence of a computer and types of internet subscription in household, total, bachelor’s degree or higher, has a computer 0

Feature shapes

shap_by_covariate <- treeshap_all %>% 
                      dplyr::select(ablation, variable, covariate_value, shap) %>%
                        right_join(top_5_vars_by_abblation, copy=T) %>%
                      #mutate(covariate_value_scaled_rounded=round(covariate_value_scaled, 1)) %>% 
                      #group_by(ablation, variable, covariate_value_scaled_rounded) %>%
                      #summarise(shape_mean=mean(shap)) %>% 
                      collect()
## Joining, by = c("ablation", "variable")
dim(shap_by_covariate) #8,748,504
## [1] 118260      9
top_5_shap_by_covariate <- 
shap_by_covariate %>%
  arrange(ablation, shap_cluster_total %>% desc() ) %>%
  mutate(shap_variable_total_rank = (-1*shap_variable_total) %>% as.factor() %>% as.numeric() ) %>%
  mutate(ablation_rank= ablation*1000 +  shap_variable_total_rank) %>%
  mutate(ablation_variable_i =  ablation_rank %>% as.factor()  %>% as.numeric() ) %>%
  arrange(ablation_variable_i)
  #dplyr::select(ablation,variable,shap_variable_total_rank) %>% distinct()

for(i in unique(top_5_shap_by_covariate$ablation_variable_i)){
  
  p <- top_5_shap_by_covariate %>% 
        dplyr::filter(ablation_variable_i==i) %>% 
        ggplot(aes(x=covariate_value %>% scale(),y=shap)) +
        #geom_point() + 
        geom_quantile(method = "rqss", lambda = 0.1, quantiles = c(0.05, 0.95), na.rm=T, linetype=c('solid'), color="black", size=0.25) +
        geom_quantile(method = "rqss", lambda = 0.1, quantiles = c(0.5), na.rm=T, linetype=c('solid'), color="black") +
        theme_void() + 
        geom_hline(yintercept=0, linetype="dashed", color = "blue", size=0.25) + 
        geom_vline(xintercept=0, linetype="dashed", color = "blue", size=0.25)
    
  ggsave(filename=glue::glue("/mnt/8tb_a/rwd_github_private/TrumpSupportVaccinationRates/docs/plots/p_ablation_marginal_fx_{i}.png"),
         plot = p , height=0.5, width=1)
}
## Smoothing formula not specified. Using: y ~ qss(x, lambda = 0.1)
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## Warning in rq.fit.sfn(x, y, tau = tau, rhs = rhs, control = control, ...): tiny diagonals replaced with Inf when calling blkfct
## Smoothing formula not specified. Using: y ~ qss(x, lambda = 0.1)
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## Warning in rq.fit.sfn(x, y, tau = tau, rhs = rhs, control = control, ...): tiny diagonals replaced with Inf when calling blkfct
## Smoothing formula not specified. Using: y ~ qss(x, lambda = 0.1)
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## Warning in rq.fit.sfn(x, y, tau = tau, rhs = rhs, control = control, ...): tiny diagonals replaced with Inf when calling blkfct
## Smoothing formula not specified. Using: y ~ qss(x, lambda = 0.1)
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## Warning in rq.fit.sfn(x, y, tau = tau, rhs = rhs, control = control, ...): tiny diagonals replaced with Inf when calling blkfct
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#top_5_shap_by_covariate <- shap_by_covariate %>%
#                            arrange(ablation, shap_cluster_total %>% desc(),
#                                    shap_variable_total %>% desc(),covariate_value_scaled_rounded) %>%
#                            mutate(groups=paste0(ablation,variable))
#top_5_shap_by_covariate_list <- split(top_5_shap_by_covariate$shape_mean,top_5_shap_by_covariate$groups)
#depricated now
#, results="asis"
#of O remder to html it works but not other scenario
library(tidyverse)
library(kableExtra)
setwd("/mnt/8tb_a/rwd_github_private/TrumpSupportVaccinationRates/docs")
library(here)


  #kable_styling(latex_options = c("hold_position", "repeat_header"), bootstrap_options = c('striped')) %>%

  #column_spec(5, 
  #            image = spec_image(
  #                #You have to knit to html and you have to use the file:/// prefix or it all breaks
  #                sapply(performance_ablation$ablation, FUN=function(i) glue::glue("file:///mnt/8tb_a/rwd_github_private/TrumpSupportVaccinationRates/docs/plots/p_ablation_states_{i}.png" )),
  #                400, 200)
  #           ) %>%
  #column_spec(3, width = "10cm" ) %>%


performance_ablation %>% 
  left_join(number_features %>% dplyr::select(ablation,feature_count_mean)) %>%
  dplyr::select(Round=ablation , RMSE=rmse_y_hat_test , MAE=rae_y_hat_test, Features=feature_count_mean ) %>%
  
  mutate(Features= round(Features,0)) %>%
  
  mutate(RMSE= round(RMSE*100,2 )) %>%
  mutate(MAE= round(MAE*100,2 )) %>%
  mutate(MAE_Delta ="") %>%
  
  #mutate(RMSE =  color_bar("orange")(RMSE  )  ) %>% #too close doesn't look good
  #mutate(MAE =  color_bar("blue")(MAE )  ) %>%
  kbl(format='html',booktabs = TRUE, longtable = TRUE, escape = F) %>% #, align = 'c'
  kable_paper(full_width = F) %>%
  kable_styling(latex_options = c("hold_position", "repeat_header"), bootstrap_options = c('striped')) %>%

  column_spec(5, 
              image = spec_image(
                  #You have to knit to html and you have to use the file:/// prefix or it all breaks
                  sapply(performance_ablation$ablation, FUN=function(i) glue::glue("file:///mnt/8tb_a/rwd_github_private/TrumpSupportVaccinationRates/docs/plots/p_ablation_states_{i}.png" )),
                  400, 200)
             ) %>%
  column_spec(6, image = spec_plot(mpg_list, same_lim = TRUE)) %>%

Combine the two tables

temp<- 
performance_ablation %>% 
  left_join(number_features %>% dplyr::select(ablation,feature_count_mean)) %>%
  left_join(
    top_5_vars_by_abblation %>% 
        dplyr::select(ablation,k_smallest,description_clean,shap=shap_variable_total)
  ) %>%
  mutate(MAE_Delta ="") %>%
  dplyr::select(ablation , rmse_y_hat_test , rae_y_hat_test, feature_count_mean, MAE_Delta, description_clean, shap) %>%
  mutate(feature_count_mean= round(feature_count_mean,0)) %>%
  mutate(rmse_y_hat_test= round(rmse_y_hat_test*100,2 )) %>%
  mutate(rae_y_hat_test= round(rae_y_hat_test*100,2 )) %>%
  mutate(shap= round(shap )) %>%
  #Alternatively there's a markdown way to do this
  #https://www.titanwolf.org/Network/q/a471f9e2-3921-4292-bd69-467a36114657/y
  mutate(MAE_Delta= sprintf("![](%s)", 
                          sapply(top_5_vars_by_abblation$ablation, FUN=function(i) glue::glue("file:///mnt/8tb_a/rwd_github_private/TrumpSupportVaccinationRates/docs/plots/p_ablation_states_{i}.png" )) )  ) %>%
  #mutate(RMSE =  color_bar("orange")(RMSE  )  ) %>% #too close doesn't look good
  #mutate(MAE =  color_bar("blue")(MAE )  ) %>%
  mutate(MarginalFX=glue::glue("![](file:///mnt/8tb_a/rwd_github_private/TrumpSupportVaccinationRates/docs/plots/p_ablation_marginal_fx_{row_number()}.png)"))  
## Joining, by = "ablation"
## Joining, by = "ablation"
dim(temp)
## [1] 90  8
#ggsave(filename=glue::glue("/mnt/8tb_a/rwd_github_private/TrumpSupportVaccinationRates/docs/plots/p_ablation_marginal_fx_{i}.png"), plot = p , height=0.5, width=1)
temp %>%
  kbl(format='html', padding=0 ,
      booktabs = TRUE,
      longtable = TRUE, 
      escape = F,
      align = c('l', 'c', 'c', 'c', 'c', 'l', 'c'), 
      col.names = c("", "RMSE", "MAE", "Feat.","Δ MAE by St.","Feature","Shap","Marginal Fx.")
      ) %>% 
  kable_paper("striped", full_width = F) %>%
  kable_styling(bootstrap_options="condensed") %>%
  row_spec(row=0, angle = 0, align='l', hline_after=T) %>%
  add_header_above(c(" " = 1, "Model Performance (Cumulative Ablation of Top Performing Feature Clusters)" = 4, "Feature Performance (Top Cluster)" = 3)) %>%
  column_spec(5, width = "4cm" )  %>%
  column_spec(8, width = "1cm" )  %>%
  #column_spec(8, image = spec_plot(top_5_shap_by_covariate_list, same_lim = F)) %>%
  collapse_rows(columns = 1:5, valign = "top")
Model Performance (Cumulative Ablation of Top Performing Feature Clusters)
Feature Performance (Top Cluster)
RMSE MAE Feat. Δ MAE by St.  Feature Shap Marginal Fx.
1 1.12 0.37 74 WARNING DV ON RHS people partial covidactnow 18plus 68
WARNING DV ON RHS people partial vaccinetracking 18plus 39
WARNING DV ON RHS people complete usatoday 18plus 4
2 5.95 4.70 295 who self report somewhat/strongly support providing tax rebates for people who purchase energy-efficient vehicles or solar panels 38
who self report somewhat/strongly support regulating CO2 as a pollutant 1
who self report somewhat/strongly support requiring utilities to produce 20% electricity from renewable sources 1
3 6.06 4.73 195 politics Trump Vote 2016 30
politics Trump Vote 2020 15
4 6.26 4.89 144 who self report somewhat/strongly agree that global warming is affecting the weather in the United States 28
who self report somewhat/strongly disagree that global warming is affecting the weather in the United States 26
who self report think their local officials should be doing more/much more to address global warming 10
who self report somewhat/strongly disagree that schools should teach about the causes, consequences, and potential solutions to global warming 6
who self report think Congress should be doing more/much more to address global warming 2
5 5.99 4.70 315 Total fields of bachelor’s degrees reported, total, science and engineeringsocial sciences 20
Total fields of bachelor’s degrees reported, total, arts, humanities, and otherliterature and languages 6
6 5.85 4.59 314 Health insurance coverage status by age, total, 19 to 64 years, no health insurance coverage 19
Health insurance coverage status by age, total,≥19 years, no health insurance coverage 4
Health insurance coverage status by age, total, 19 to 64 years, no health insurance coverage 1
7 6.04 4.74 238 religion evangelical lutheran church in america–total number of congregations 5
religion mainline protestant–total number of congregations 5
religion evangelical protestant 4
religion mainline protestant 4
religion seventh-day adventist church 2
8 6.01 4.71 239 Place of birth by educational attainment in the united states, total, graduate or professional degree 13
Place of birth by age in the united states, total, native; born outside the united states, 25 to 34 years 2
Place of birth by educational attainment in the united states, total, born in other state in the united states, high school graduate 2
Place of birth by educational attainment in the united states, total, born in state of residence, graduate or professional degree 2
Place of birth by age in the united states, total, born in other state in the united states,≥5 years 1
9 5.98 4.69 144 Geographical mobility in the past year by educational attainment for current residence in the united states, total, graduate or professional degree 10
Geographical mobility in the past year by educational attainment for residence 1 year ago in the united states, total living in area 1 year ago, same house, graduate or professional degree 4
Geographical mobility in the past year by educational attainment for current residence in the united states, total, moved within same county, bachelor’s degree 2
Geographical mobility in the past year by age for current residence in the united states, total, moved from different state, 1 to 4 years 2
Geographical mobility in the past year by marital status for residence 1 year ago in the united states, total living in area 1 year ago, moved to different county within same state, now married, except separated 2
10 5.96 4.72 540 Δ employed by Private in NAICS 72 Accommodation and food services 14
Δ employed by Private in NAICS 71 Arts, entertainment, and recreation 2
people employed by Federal Government in NAICS 48-49 Transportation and warehousing 1
Δ employed by Private in NAICS 23 Construction 1
people employed by Private in NAICS 11 Agriculture, forestry, fishing and hunting 0
11 6.07 4.73 200 people who self report masking always 13
people who self report masking never 4
people who self report masking rarely 2
people who self report masking sometimes 2
12 6.20 4.90 247 Field of bachelor’s degree for first major for the population 25 11
Sex by age by field of bachelor’s degree for first major for the population 25 3
Sex by age by field of bachelor’s degree for first major for the population 25 2
Sex by age by field of bachelor’s degree for first major for the population 25 2
Sex by age by field of bachelor’s degree for first major for the population 25 1
13 6.26 4.97 237 Mortgage status, total, housing units with a mortgage, contract to purchase, or similar debt, with either a second mortgage or home equity loan, but not both 6
Mortgage status, total, housing units with a mortgage, contract to purchase, or similar debt, with either a second mortgage or home equity loan, but not both, home equity loan only 5
Mortgage status by real estate taxes paid, total, not mortgaged, no real estate taxes paid 3
Mortgage status by real estate taxes paid, total, not mortgaged, less than $800 3
Mortgage status by real estate taxes paid, total, with a mortgage, no real estate taxes paid 2
14 6.22 4.92 265 flu vaccinations medicare 8
vaccine supply locations walkin 2
people self report would accept vaccination 1
COVID infections total infected mean 1
COVID infections ratio estimated cases to measured 1
15 6.28 4.93 135 Health insurance coverage status and type by work experience, total, worked less than full-time, year-round, with health insurance coveragewith medicaid/means-tested public coverage 9
Health insurance coverage status and type by work experience, total, worked full-time, year-round, no health insurance coverage 2
Health insurance coverage status and type by work experience, total, worked full-time, year-round, with health insurance coveragewith medicaid/means-tested public coverage 1
Health insurance coverage status and type by employment status, total, not in labor force, no health insurance coverage 1
Health insurance coverage status and type by work experience, total, worked less than full-time, year-round, no health insurance coverage 1
16 6.26 4.90 127 Veteran status by educational attainment for the civilian population 25 12
Service-connected disability-rating status and ratings for civilian veterans 18 1
Age by veteran status by employment status for the civilian population 18 to 64 years, total, 35 to 54 years, nonveteran, in labor force 1
17 6.19 4.84 307 Health insurance coverage status and type by citizenship status, total, native born, no health insurance coverage 14
Health insurance coverage status by living arrangement, total, in family households, in married couple families, no health insurance coverage 1
18 4.90 248 who self report somewhat/strongly oppose drilling for oil in the Arctic National Wildlife Refuge 9
who self report somewhat/strongly oppose expanding offshore drilling for oil and natural gas off the U.S. coast 5
who self report somewhat/strongly support drilling for oil in the Arctic National Wildlife Refuge 1
who self report somewhat/strongly support expanding offshore drilling for oil and natural gas off the U.S. coast 1
GDP in mining, quarrying, and oil and gas extraction 0
19 6.28 4.95 310 Citizen, voting-age population by educational attainment, total, graduate or professional degree 15
Citizen, voting-age population by educational attainment, total, some college, no degree 1
20 6.31 5.01 545 Educational attainment for the population 25 20
Educational attainment for the population 25 0
Educational attainment for the population 25 0
21 6.33 176 Types of health insurance coverage by age, total, 35 to 64 years, with one type of health insurance coverage, with medicaid/means-tested public coverage only 4
Types of health insurance coverage by age, total, 35 to 64 years, no health insurance coverage 3
Types of health insurance coverage by age, total, 19 to 34 years, with one type of health insurance coverage, with medicaid/means-tested public coverage only 2
Types of health insurance coverage by age, total, 35 to 64 years, with one type of health insurance coverage, with va health care only 2
Health insurance coverage status and type by age by educational attainment, total, 26 to 64 years, some college or associate’s degree 2
22 6.19 4.87 297 Sex by educational attainment for the population 25 6
Sex by age by educational attainment for the population 18 4
Sex by educational attainment for the population 25 3
Sex by educational attainment for the population 25 2
Sex by age by educational attainment for the population 18 1
23 6.30 5.01 226 Educational attainment by presence of a computer and types of internet subscription in household, total, bachelor’s degree or higher, has a computer, with a broadband internet subscription 18
Educational attainment by presence of a computer and types of internet subscription in household, total, bachelor’s degree or higher 5
Educational attainment by presence of a computer and types of internet subscription in household, total, bachelor’s degree or higher, has a computer 0